Reservoir characterization is a process of using well log and seismic data to map reservoir geometry, net-to-gross ratio, porosity, water saturation, permeability, and other petrophysical properties. Each seismic response trace is the result of a seismic source wavelet convoluted with a spatial earth reflectivity series, if linear, and is not a direct indicator of rock and fluid properties. Well logs with much higher vertical resolution than seismic traces, such as Gamma Ray, Density, Porosity, Sonic velocity, Resistivity etc. measured from borehole can be direct indicators of rock and fluid properties after some borehole environmental corrections.
The number of wells drilled in a reservoir is always limited because of well cost and the thickness of many reservoir formations is often below seismic resolution. If wells were drilled in each location of the seismic trace (or each location of geophone), then logs would be obtained from these wells and the reservoir characterization would utilize the well logs to map reservoir geometry, net-to-gross ratio, porosity, water saturation, permeability, and other petrophysical properties. For economical reasons (or cost-effectiveness), there are rarely enough wells to properly map the details of reservoirs. Therefore, Petrophysical properties are inferred form the seismic data.
Until recently, seismic attributes were extracted and calibrated with available well control data, and petrophysical properties were interpolated/extrapolated between and beyond sparse well control. A set of seismic attributes related to time, amplitude and shape, including trace-to-trace time tracking cross correlation coefficient, integrated absolute amplitude, and area ratio of fatness, were calculated and extracted from reflection seismic trace(s). Interval attributes such as, the average of all positive peak amplitudes and integrated absolute amplitude are calibrated against petrophysical properties from well logs. Rock property maps can be generated utilizing these attributes at an interval average. Though many attributes can be generated from a seismic traces, how to classify and calibrate attributes with well data for best quantitative reservoir characterization and uncertainty analysis are continual subjects of research.
U.S. Pat. No. 5,444,619 (Hoskins et al.) uses a “Artificial Neural Networks (ANN's)” method to estimate the relationship of reservoir properties and seismic data. However, the confidence level of such prediction using statistical methods are often questioned and challenged due to lack of basic physical relationships between a seismic trace and petrophysical properties indicated by a set of logs. Another method for determining reservoir properties from seismic data is disclosed in U.S. Pat. No. 5,835,883 (Neff et al.) This method requires constructing pseudo well logs and synthetic seismic traces of the seismic and well log data to produce an estimate of the petrophysical properties. The model includes a matching scheme wherein trend curves of petrophysical properties are selected based on the match to the data. This process requires operator selection and selecting the wrong trend curve can lead to errors.
Accordingly, there is a need for an innovative method to directly convert seismic data to petrophysical property data. The methods preferably includes converting three-dimensional seismic data to petrophysical property cube(s) in either time or depth domain with enhanced vertical resolution. Embodiments of this invention satisfy these needs.